I’m sure there’s some 100% subjective, hallucinated value statement but never felt a reason to bother.
I’ve only ever used an AWS SDK and “saved state” to git in the form of my SDK scripts.
Switched from Python+boto to Go+SDK a few years ago rather than learn some DSL the Lindy effect clock was ticking on.
- the problem: if something gets deleted, or if you make a bunch of changes and want to undo them, or if you need to make a change to a lot of stuff at once, or if you want to copy what you've built to a new region, how do you do it? or, if you're on a team, how do you as a team make and track changes to your cloud infrastructure?
- terraform as a solution: you describe your cloud infrastructure as yaml files. terraform can figure out what is different between what's in your cloud infrastructure and what your yaml files say it should look like. and, it can make changes to your cloud to e.g. build it from scratch, make wide-ranging changes, make a copy of it, etc.
- since your yaml files are code, you can also create a repo and do PRs to make and track changes to your cloud infrastructure as it evolves over time
Looked at the TF code; my solution implements similar functionality to handle AWS CRUD ops. What I avoid is all the DSL parsing and such.
For me an AWS account is a struct with fields of AWS SDK resource types, which it seems is what TF resources map to (they handle a lot more so there’s more to it, but kind of sort of if I squint just right). Either going to duplicate the internal logic or DSL chunks per project, would rather avoid the context switch between syntax, “learning the TF ecosystem”.
Thanks again, though.
Why just not writing idempotent resource creation? Terraform also uses this to calculate a "plan" that shows the diff of your changes with reality, which really helps to figure out what happens to your RDS when executing, especially when more abstraction (in form of Terraform modules) is involved.
We used Terraform also in a situation where writing custom code would be "prettier" but would have required to write this actual vs desired state code ourselves and could save us the work of doing so.
The DSL of Terraform is sometimes quite cumbersome though as it's derived JSON and not some actual programming language.
A simple example of what I mean by computed values is that let’s say you want to provision a k8s cluster on top of a network. The k8s provider might want the network name/id which you could normally get by setting it upstream. The problem is you can’t plan the network creation and k8s cluster in a single pass because you don’t get the network name until it’s actually provisioned. You actually need to apply the network tf first to get the inputs you need to plan the cluster. Meaning not only do you need to run tf twice, you also can’t E2E plan infra provisioning
If anyone has a solution/pattern for the above (or more generally how to chain these modules together when this limitation exists) I’m all ears
Doing that allows Terraform to create both resources in one plan/apply step, and it also helps Terraform understand the dependency between the resources so that they are created in the correct order.
For me using terraform is quicker than clicking things up or using CLI even when initially developing things, as if something goes wrong I can just destroy the state and re-apply the terraform config.
then you obviously have every single resource name, required values, and relationships between them memorized because in my experience post-facto encoding of something into TF can be valuable to the organization but trying to _discover_ the providers, iam, required fields to achieve a desired outcome is crawling over broken glass as compared to click-ops-ing something in place
Hell, there's even a browser extension to record the AWS calls so one can at least see what was done later for replay, but with GCP they have their own sneaky RPC something-something encoding so that idea's off the table
I don't.
> crawling over broken glass as compared to click-ops-ing something in place
I do not experience reading the terraform provider docs like crawling over broken glass but okay.
I program with Python also and it's not like I have memorized every class and function either, but still, I and millions of other people somehow manage get by.